From Zero to The Hero: A Collaborative Market Aware Recommendation System for Crowd Workers
Hamid Shamszare, Razieh Saremi, Sanam Jena

TL;DR
This paper introduces a collaborative recommendation system for crowd workers in software crowdsourcing, aiming to improve task success rates by predicting worker success probabilities based on multiple behavioral metrics.
Contribution
It presents a novel recommendation method that leverages collaboration history, preferences, specialty, and proficiency to enhance worker success prediction in crowdsourcing markets.
Findings
Achieved up to 86% success rate using the system
Reduced task dropping rate among crowd workers
Demonstrated effectiveness on 260 active workers
Abstract
The success of software crowdsourcing depends on active and trustworthy pool of worker supply. The uncertainty of crowd workers' behaviors makes it challenging to predict workers' success and plan accordingly. In a competitive crowdsourcing marketplace, competition for success over shared tasks adds another layer of uncertainty in crowd workers' decision-making process. Preliminary analysis on software worker behaviors reveals an alarming task dropping rate of 82.9%. These factors lead to the need for an automated recommendation system for CSD workers to improve the visibility and predictability of their success in the competition. To that end, this paper proposes a collaborative recommendation system for crowd workers. The proposed recommendation system method uses five input metrics based on workers' collaboration history in the pool, workers' preferences in taking tasks in terms of…
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Taxonomy
TopicsMobile Crowdsensing and Crowdsourcing · Auction Theory and Applications · Open Source Software Innovations
